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Pan RW, Röschinger T, Faizi K, Garcia HG, Phillips R. Deciphering regulatory architectures of bacterial promoters from synthetic expression patterns. PLoS Comput Biol 2024; 20:e1012697. [PMID: 39724021 DOI: 10.1371/journal.pcbi.1012697] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2024] [Accepted: 12/04/2024] [Indexed: 12/28/2024] Open
Abstract
For the vast majority of genes in sequenced genomes, there is limited understanding of how they are regulated. Without such knowledge, it is not possible to perform a quantitative theory-experiment dialogue on how such genes give rise to physiological and evolutionary adaptation. One category of high-throughput experiments used to understand the sequence-phenotype relationship of the transcriptome is massively parallel reporter assays (MPRAs). However, to improve the versatility and scalability of MPRAs, we need a "theory of the experiment" to help us better understand the impact of various biological and experimental parameters on the interpretation of experimental data. These parameters include binding site copy number, where a large number of specific binding sites may titrate away transcription factors, as well as the presence of overlapping binding sites, which may affect analysis of the degree of mutual dependence between mutations in the regulatory region and expression levels. To that end, in this paper we create tens of thousands of synthetic gene expression outputs for bacterial promoters using both equilibrium and out-of-equilibrium models. These models make it possible to imitate the summary statistics (information footprints and expression shift matrices) used to characterize the output of MPRAs and thus to infer the underlying regulatory architecture. Specifically, we use a more refined implementation of the so-called thermodynamic models in which the binding energies of each sequence variant are derived from energy matrices. Our simulations reveal important effects of the parameters on MPRA data and we demonstrate our ability to optimize MPRA experimental designs with the goal of generating thermodynamic models of the transcriptome with base-pair specificity. Further, this approach makes it possible to carefully examine the mapping between mutations in binding sites and their corresponding expression profiles, a tool useful not only for developing a theory of transcription, but also for exploring regulatory evolution.
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Affiliation(s)
- Rosalind Wenshan Pan
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, California, United States of America
| | - Tom Röschinger
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, California, United States of America
| | - Kian Faizi
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, California, United States of America
| | - Hernan G Garcia
- Biophysics Graduate Group, University of California, Berkeley, California, United States of America
- Department of Physics, University of California, Berkeley, California, United States of America
- Department of Molecular and Cell Biology, University of California, Berkeley, California, United States of America
- Institute for Quantitative Biosciences-QB3, University of California, Berkeley, California, United States of America
| | - Rob Phillips
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, California, United States of America
- Department of Physics, California Institute of Technology, Pasadena, California, United States of America
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Joshi SHN, Jenkins C, Ulaeto D, Gorochowski TE. Accelerating Genetic Sensor Development, Scale-up, and Deployment Using Synthetic Biology. BIODESIGN RESEARCH 2024; 6:0037. [PMID: 38919711 PMCID: PMC11197468 DOI: 10.34133/bdr.0037] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2024] [Accepted: 04/23/2024] [Indexed: 06/27/2024] Open
Abstract
Living cells are exquisitely tuned to sense and respond to changes in their environment. Repurposing these systems to create engineered biosensors has seen growing interest in the field of synthetic biology and provides a foundation for many innovative applications spanning environmental monitoring to improved biobased production. In this review, we present a detailed overview of currently available biosensors and the methods that have supported their development, scale-up, and deployment. We focus on genetic sensors in living cells whose outputs affect gene expression. We find that emerging high-throughput experimental assays and evolutionary approaches combined with advanced bioinformatics and machine learning are establishing pipelines to produce genetic sensors for virtually any small molecule, protein, or nucleic acid. However, more complex sensing tasks based on classifying compositions of many stimuli and the reliable deployment of these systems into real-world settings remain challenges. We suggest that recent advances in our ability to precisely modify nonmodel organisms and the integration of proven control engineering principles (e.g., feedback) into the broader design of genetic sensing systems will be necessary to overcome these hurdles and realize the immense potential of the field.
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Affiliation(s)
| | - Christopher Jenkins
- CBR Division, Defence Science and Technology Laboratory, Porton Down, Wiltshire SP4 0JQ, UK
| | - David Ulaeto
- CBR Division, Defence Science and Technology Laboratory, Porton Down, Wiltshire SP4 0JQ, UK
| | - Thomas E. Gorochowski
- School of Biological Sciences, University of Bristol, Bristol BS8 1TQ, UK
- BrisEngBio,
School of Chemistry, University of Bristol, Bristol BS8 1TS, UK
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Pan RW, Röschinger T, Faizi K, Garcia H, Phillips R. Deciphering regulatory architectures from synthetic single-cell expression patterns. ARXIV 2024:arXiv:2401.15880v2. [PMID: 38351929 PMCID: PMC10862939] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Figures] [Subscribe] [Scholar Register] [Indexed: 02/19/2024]
Abstract
For the vast majority of genes in sequenced genomes, there is limited understanding of how they are regulated. Without such knowledge, it is not possible to perform a quantitative theory-experiment dialogue on how such genes give rise to physiological and evolutionary adaptation. One category of high-throughput experiments used to understand the sequence-phenotype relationship of the transcriptome is massively parallel reporter assays (MPRAs). However, to improve the versatility and scalability of MPRA pipelines, we need a "theory of the experiment" to help us better understand the impact of various biological and experimental parameters on the interpretation of experimental data. These parameters include binding site copy number, where a large number of specific binding sites may titrate away transcription factors, as well as the presence of overlapping binding sites, which may affect analysis of the degree of mutual dependence between mutations in the regulatory region and expression levels. To that end, in this paper we create tens of thousands of synthetic single-cell gene expression outputs using both equilibrium and out-of-equilibrium models. These models make it possible to imitate the summary statistics (information footprints and expression shift matrices) used to characterize the output of MPRAs and from this summary statistic to infer the underlying regulatory architecture. Specifically, we use a more refined implementation of the so-called thermodynamic models in which the binding energies of each sequence variant are derived from energy matrices. Our simulations reveal important effects of the parameters on MPRA data and we demonstrate our ability to optimize MPRA experimental designs with the goal of generating thermodynamic models of the transcriptome with base-pair specificity. Further, this approach makes it possible to carefully examine the mapping between mutations in binding sites and their corresponding expression profiles, a tool useful not only for better designing MPRAs, but also for exploring regulatory evolution.
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Affiliation(s)
- Rosalind Wenshan Pan
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA
| | - Tom Röschinger
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA
| | - Kian Faizi
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA
| | - Hernan Garcia
- Biophysics Graduate Group, University of California, Berkeley, CA
- Department of Physics, University of California, Berkeley, CA
- Department of Molecular and Cell Biology, University of California, Berkeley, CA
- Institute for Quantitative Biosciences-QB3, University of California, Berkeley, CA
| | - Rob Phillips
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA
- Department of Physics, California Institute of Technology, Pasadena, CA
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Pan RW, Röschinger T, Faizi K, Garcia H, Phillips R. Deciphering regulatory architectures from synthetic single-cell expression patterns. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.01.28.577658. [PMID: 38352569 PMCID: PMC10862715 DOI: 10.1101/2024.01.28.577658] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 02/22/2024]
Abstract
For the vast majority of genes in sequenced genomes, there is limited understanding of how they are regulated. Without such knowledge, it is not possible to perform a quantitative theory-experiment dialogue on how such genes give rise to physiological and evolutionary adaptation. One category of high-throughput experiments used to understand the sequence-phenotype relationship of the transcriptome is massively parallel reporter assays (MPRAs). However, to improve the versatility and scalability of MPRA pipelines, we need a "theory of the experiment" to help us better understand the impact of various biological and experimental parameters on the interpretation of experimental data. These parameters include binding site copy number, where a large number of specific binding sites may titrate away transcription factors, as well as the presence of overlapping binding sites, which may affect analysis of the degree of mutual dependence between mutations in the regulatory region and expression levels. To that end, in this paper we create tens of thousands of synthetic single-cell gene expression outputs using both equilibrium and out-of-equilibrium models. These models make it possible to imitate the summary statistics (information footprints and expression shift matrices) used to characterize the output of MPRAs and from this summary statistic to infer the underlying regulatory architecture. Specifically, we use a more refined implementation of the so-called thermodynamic models in which the binding energies of each sequence variant are derived from energy matrices. Our simulations reveal important effects of the parameters on MPRA data and we demonstrate our ability to optimize MPRA experimental designs with the goal of generating thermodynamic models of the transcriptome with base-pair specificity. Further, this approach makes it possible to carefully examine the mapping between mutations in binding sites and their corresponding expression profiles, a tool useful not only for better designing MPRAs, but also for exploring regulatory evolution.
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Affiliation(s)
- Rosalind Wenshan Pan
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA
| | - Tom Röschinger
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA
| | - Kian Faizi
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA
| | - Hernan Garcia
- Biophysics Graduate Group, University of California, Berkeley, CA
- Department of Physics, University of California, Berkeley, CA
- Department of Molecular and Cell Biology, University of California, Berkeley, CA
- Institute for Quantitative Biosciences-QB3, University of California, Berkeley, CA
| | - Rob Phillips
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA
- Department of Physics, California Institute of Technology, Pasadena, CA
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